Continuous probability distributions describe the probabilities of outcomes for continuous random variables, which can take any value within a given range. Unlike discrete distributions, where outcomes are distinct and countable, continuous distributions represent probabilities over intervals. The total area under the curve of a continuous probability distribution equals 1, indicating that some outcome will occur. Common examples of continuous probability distributions include the normal distribution, uniform distribution, and exponential distribution. These distributions are often used in statistics and various fields to model real-world phenomena, such as heights, weights, and measurement errors, where values can vary smoothly rather than in fixed steps.